Improvement of trajectory tracking performance via modified composite nonlinear control

Author(s):  
Yufang Shi ◽  
Guoyang Cheng ◽  
Yanwei Huang ◽  
Jin-gao Hu
2021 ◽  
Author(s):  
Manjeet Tummalapalli

This project proposes a new SCARA variant with 4 degree of freedom. The proposed variant is achieved by swapping joint 2 and joint 3 of the standard SCARA robots. An adaptive controller is defined based on the advantages and disadvantages of PD, and SMC controllers.The purpose of the project is to understand the dynamics of the variant and to track the performance for trajectories. Simulations for tracking performance are carried under linear and circular trajectories. The variant is studied over the three controllers; PD, PD-SMC and A-PD-SMC. The variant under the adaptive controller is most efficient in terms of tracking performance and the control inputs to the system. The system is simulated under high speed and with the influence of friction at the joints. The control gains are held constant for both the trajectories and hence the controller is able to perform good under changing trajectories. Due to the use of the adaptive law, the system is at the ease of implementation and since no priori knowledge if the system is needed, it is model free. Therefore, the proposed adaptive PD-SMC has proven to provide good, robust trajectory tracking.


Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.


2011 ◽  
Vol 110-116 ◽  
pp. 3176-3183 ◽  
Author(s):  
Mao Hsiung Chiang ◽  
Hao Ting Lin

This study aims to develop a leveling position control of an active PWM-controlled pneumatic isolation table system. A novel concept using parallel dual-on/off valves with PWM control signals is implemented to realize active control and to improve the conventional pneumatic isolation table that supported by four pneumatic cushion isolators. In this study, the cushion isolators are not only passive vibration isolation devices, but also pneumatic actuators in active position control. Four independent closed-loop position feedback control system are designed and implemented for the four axial isolators. In this study, on/off valves are used, and PWM is realized by software. Therefore, additional hardware circuit is not required to implement PWM and not only cost down but also reach control precision of demand. In the controller design, the Fourier series-based adaptive sliding-mode controller with H∞ tracking performance is used to deal with the uncertainty and time-varying problems of pneumatic system. Finally, the experiments on the pneumatic isolation table system for synchronous position and trajectory tracking control, including no-load and loading conditions, and synchronous position control with master-slave method, are implemented in order to verify that the controller for each cushion isolator can realize good position and trajectory tracking performance.


2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
N. Ramos-Pedroza ◽  
W. MacKunis ◽  
M. Reyhanoglu

A robust nonlinear control law that achieves trajectory tracking control for unmanned aerial vehicles (UAVs) equipped with synthetic jet actuators (SJAs) is presented in this paper. A key challenge in the control design is that the dynamic characteristics of SJAs are nonlinear and contain parametric uncertainty. The challenge resulting from the uncertain SJA actuator parameters is mitigated via innovative algebraic manipulation in the tracking error system derivation along with a robust nonlinear control law employing constant SJA parameter estimates. A key contribution of the paper is a rigorous analysis of the range of SJA actuator parameter uncertainty within which asymptotic UAV trajectory tracking can be achieved. A rigorous stability analysis is carried out to prove semiglobal asymptotic trajectory tracking. Detailed simulation results are included to illustrate the effectiveness of the proposed control law in the presence of wind gusts and varying levels of SJA actuator parameter uncertainty.


2016 ◽  
Vol 146 ◽  
pp. 149-164 ◽  
Author(s):  
Gokhan Bayar ◽  
Marcel Bergerman ◽  
Erhan i. Konukseven ◽  
Ahmet B. Koku

Robotica ◽  
2016 ◽  
Vol 35 (7) ◽  
pp. 1488-1503 ◽  
Author(s):  
Vikas Panwar

SUMMARYThis paper focuses on fast terminal sliding mode control (FTSMC) of robot manipulators using wavelet neural networks (WNN) with guaranteed H∞tracking performance. The FTSMC for trajectory tracking is employed to drive the tracking error of the system to converge to an equilibrium point in finite time. The tracking error arrives at the sliding surface in finite time and then converges to zero in finite time along the sliding surface. To deal with the case of uncertain and unknown robot dynamics, a WNN is proposed to fully compensate the robot dynamics. The online tuning algorithms for the WNN parameters are derived using Lyapunov approach. To attenuate the effect of approximation errors to a prescribed level, H∞tracking performance is proposed. It is shown that the proposed WNN is able to learn the system dynamics with guaranteed H∞tracking performance and finite time convergence for trajectory tracking. Finally, the simulation results are performed on a 3D-Microbot manipulator to show the effectiveness of the controller.


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